Is your martech evaluation process still stuck in a pre-AI world?

 

 

Your martech vendor evaluation process doesn’t work anymore—not because it lacks rigor, but because it’s rooted in outdated assumptions about the market, the tools and your needs.

The martech landscape has exploded beyond what anyone can reasonably evaluate, and every tool in it claims AI capabilities. Your email platform promises AI-powered subject line optimization. Your analytics dashboard offers AI-generated insights. Your CMS features AI workflow automation.

How do you evaluate AI features when they’re embedded in everything, even your coffee maker (GE offers a drip machine that uses Google Cloud AI to help you “brew the perfect cup each morning”)?

You can’t compare tools with AI versus tools without AI anymore. That comparison doesn’t exist. You can only compare different implementations of AI within tools you were already trying to evaluate on dozens of other criteria.

The evaluation challenge has multiplied exponentially, and most marketing leaders haven’t adjusted their vendor selection process to match.

The comparison that vanished

Three years ago, AI in martech was a differentiator. If a vendor offered predictive analytics or natural language processing, that set them apart from competitors. You could evaluate whether paying more for AI capabilities made sense for your use case.

Today, AI is table stakes. The market sent a clear message to vendors: AI integration or obsolescence.

Vendors heard that message loud and clear. Now they all claim AI capabilities, which means the presence of AI tells you nothing useful about whether a tool will solve your problems.

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Your evaluation process needs to shift from asking “Does this tool have AI?” to asking far more difficult questions about implementation quality, genuine capabilities versus rebranded automation, and measurable outcomes.

The AI washing problem

Here’s what makes this evaluation crisis worse: many vendors slapped “AI-powered” labels on features that are automation rebranded with trendy terminology.

The difference matters. Automation follows predetermined rules and produces predictable outputs. AI adapts based on data, learns from patterns, and improves performance over time. One is a flowchart. The other is a system that gets smarter.

The Federal Trade Commission launched Operation AI Comply to crack down on deceptive AI claims, issuing multiple enforcement actions against companies making false assertions about their AI capabilities. The regulatory scrutiny exists because the problem is widespread.

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When vendors obscure the distinction between rule-based automation and adaptive AI, your evaluation becomes guesswork. You’re comparing claims, not capabilities.

That analytics dashboard promising AI-generated insights might be running basic statistical analysis with predetermined thresholds. That personalization engine claiming to predict customer behavior might be triggering content based on simple segmentation rules.

Your job is to distinguish genuine AI implementation from marketing spin, which means asking questions most vendors would prefer you didn’t.

The new evaluation framework

Evaluating AI implementation quality demands different questions than traditional feature comparison. Here are five critical questions that separate genuine AI capability from vendor hype:

• What problem does this AI solve? Skip the capabilities tour and start with outcomes. If a vendor can’t articulate the specific business problem their AI addresses, they probably built AI because competitors did, not because it solves a meaningful problem.
• What does the AI learn from? Genuine AI requires data to improve performance. Ask what data feeds the system, how often it updates its models, and whether you’ll see performance improvements over time. If the vendor can’t explain the learning mechanism, you are likely looking at automation with an AI label.
• How do you prove it works? Demand quantifiable metrics that demonstrate AI performance. If they show you a dashboard of features instead of outcome data, that’s a red flag. AI’s value lies in measurable outcomes, such as improved conversion rates, higher-quality leads, or increased return on ad spend, not in the mere presence of AI capabilities. Most implementations produce impressive demos but disappointing results because vendors can’t prove the AI delivers incremental impact.
• What control do I have? AI systems that operate as black boxes create governance nightmares. You need visibility into how decisions get made, the ability to override automated actions, and clear explanations when AI produces unexpected results. Ask about model transparency, explainability features, and user controls before making a commitment.
• What happens when it’s wrong? AI will make mistakes. The question is whether the vendor has built systems to detect, correct, and learn from those mistakes. Request their approach to hallucination prevention, bias detection, and error handling. Their answer reveals whether they’ve seriously thought through implementation or bolted AI onto existing products without considering the consequences.

These questions won’t appear on vendor-provided comparison matrices. That’s the point. Standard evaluation criteria assume all AI is created equal. Your job is to prove otherwise.

The resource reality

Your new evaluation framework requires resources most marketing teams don’t have.

You need people who understand both technical AI concepts and business outcomes. You need time to run proof-of-concept tests that validate vendor claims. You need governance frameworks to manage multiple AI systems working across your martech stack.

Only 10% of marketers feel they’re using AI effectively, despite widespread adoption. That gap reveals the real problem: organizations rushed to adopt AI without developing the necessary capabilities to evaluate, implement, and operationalize it effectively.

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Treating AI evaluation as a side project for already-maxed-out staff guarantees poor vendor selection. You’ll default to whichever vendor has the slickest demo or the most aggressive sales team, not the one whose AI implementation solves your actual problems.

The companies that succeed dedicate real resources to evaluation:

• Cross-functional teams assessing vendor claims
• Structured pilots measuring actual performance
• Governance frameworks ensuring AI systems work together instead of creating new silos

Those who fail treat AI vendor selection like traditional martech buying, checking feature boxes on comparison spreadsheets without verifying whether the AI actually delivers promised outcomes.

What this means for your next martech purchase

Your next martech purchase will be harder than your last one, not easier.

The explosion of AI-powered tools didn’t simplify your options. It multiplied the complexity of evaluating those options by requiring you to assess AI implementation quality alongside traditional selection criteria.

You can’t outsource this evaluation to analyst reports or peer recommendations. Your vendor selection needs to focus on implementation fit and real-world capability, not feature checklists and glossy proposals. What works brilliantly for a competitor might fail in your organization.

Dig deeper: An outcome-driven framework for core martech selection

The good news? Your competitors face the same evaluation crisis. Most will default to brand recognition, analyst endorsements, or whatever tool their network recommends. That creates an opportunity for marketing leaders willing to build rigorous evaluation processes that separate genuine AI capabilities from vendor hype.

Your martech stack doesn’t need the most sophisticated AI. It requires AI implementations that solve real problems, integrate cleanly with your existing systems, and deliver measurable outcomes your team can prove.

Start there, and you’ll build a competitive advantage while everyone else chases the shiniest new AI feature they saw at a conference.

The post Is your martech evaluation process still stuck in a pre-AI world? appeared first on MarTech.

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